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State (Hydrodynamics) Identification in the Lower St. Johns River using the Ensemble Kalman filter

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Date Issued:
2012
Abstract/Description:
This thesis presents a method, Ensemble Kalman Filter (EnKF), applied to a high-resolution, shallow water equations model (DG ADCIRC-2DDI) of the Lower St. Johns River with observation data at four gauging stations. EnKF, a sequential data assimilation method for non-linear problems, is developed for tidal flow simulation for estimation of state variables, i.e., water levels and depth-integrated currents for overland unstructured finite element meshes. The shallow water equations model is combined with observation data, which provides the basis of the EnKF applications. In this thesis, EnKF is incorporated into DG ADCIRC-2DDI code to estimate the state variables.Upon its development, DG ADCIRC-2DDI with EnKF is first validated by implementing to a low-resolution, shallow water equations model of a quarter annular harbor with synthetic observation data at six gauging stations. Second, DG ADCIRC-2DDI with EnKF is implemented to a high-resolution, shallow water equations model of the Lower St. Johns River with real observation data at four gauging stations. Third, four different experiments are performed by applying DG ADCIRC-2DDI with EnKF to the Lower St. Johns River.
Title: State (Hydrodynamics) Identification in the Lower St. Johns River using the Ensemble Kalman filter.
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Name(s): Tamura, Hitoshi, Author
Hagen, Scott, Committee Chair
Wang, Dingbao, Committee CoChair
Bacopoulos, Peter, Committee Member
, Committee Member
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2012
Publisher: University of Central Florida
Language(s): English
Abstract/Description: This thesis presents a method, Ensemble Kalman Filter (EnKF), applied to a high-resolution, shallow water equations model (DG ADCIRC-2DDI) of the Lower St. Johns River with observation data at four gauging stations. EnKF, a sequential data assimilation method for non-linear problems, is developed for tidal flow simulation for estimation of state variables, i.e., water levels and depth-integrated currents for overland unstructured finite element meshes. The shallow water equations model is combined with observation data, which provides the basis of the EnKF applications. In this thesis, EnKF is incorporated into DG ADCIRC-2DDI code to estimate the state variables.Upon its development, DG ADCIRC-2DDI with EnKF is first validated by implementing to a low-resolution, shallow water equations model of a quarter annular harbor with synthetic observation data at six gauging stations. Second, DG ADCIRC-2DDI with EnKF is implemented to a high-resolution, shallow water equations model of the Lower St. Johns River with real observation data at four gauging stations. Third, four different experiments are performed by applying DG ADCIRC-2DDI with EnKF to the Lower St. Johns River.
Identifier: CFE0004331 (IID), ucf:49455 (fedora)
Note(s): 2012-05-01
M.S.
Engineering and Computer Science, Civil, Environmental and Construction Engineering
Masters
This record was generated from author submitted information.
Subject(s): Ensemble Kalman Filter -- Data Assimilation -- DG ADCIRC-2DDI -- State Estimation -- Lower St. Johns River -- Quarter Annular Harbor
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0004331
Restrictions on Access: public 2012-05-15
Host Institution: UCF

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